Add documentation for models
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- [examples.md, Examples]
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- [optimizers.md, Optimizers]
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- [objectives.md, Objectives]
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- [models.md, Models]
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- [activations.md, Activations]
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- [initializations.md, Initializations]
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- [Optimizers](optimizers.md)
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- [Objectives](objectives.md)
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- [Models](models.md)
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- [Activations](activations.md)
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- [Initializations](initializations.md)
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- [Datasets](datasets.md)
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## Sequential
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Linear stack of layers.
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```python
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model = keras.models.Sequential()
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```
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- __Methods__:
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- __add(layer)__: Add a layer to the model.
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- __compile(optimizer, loss, class_mode="categorical")__:
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- __Arguments__:
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- __optimizer__: str (name of optimizer) or optimizer object. See [optimizers](optimizers.md).
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- __loss__: str (name of objective function) or objective function. See [objectives](objectives.md).
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- __class_mode__: one of "categorical", "binary". This is only used for computing classification accuracy or using the predict_classes method.
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- __fit(X, y, batch_size=128, nb_epoch=100, verbose=1, validation_split=0., validation_data=None, shuffle=True, show_accuracy=False)__: Train a model for a fixed number of epochs.
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- __Arguments__:
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- __X__: data.
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- __y__: labels.
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- __batch_size__: int. Number of samples per gradient update.
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- __nb_epoch__: int.
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- __verbose__: 0 for no logging to stdout, 1 for progress bar logging, 2 for one log line per epoch.
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- __validation_split__: float (0. < x < 1). Fraction of the data to use as held-out validation data.
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- __validation_data__: tuple (X, y) to be used as held-out validation data. Will override validation_split.
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- __shuffle__: boolean. Whether to shuffle the samples at each epoch.
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- __show_accuracy__: boolean. Whether to display class accuracy in the logs to stdout at each epoch.
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- __evaluate(X, y, batch_size=128, show_accuracy=False, verbose=1)__: Show performance of the model over some validation data.
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- __Arguments__: Same meaning as fit method above. verbose is used as a binary flag (progress bar or nothing).
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- __predict_proba(X, batch_size=128, verbose=1)__: Return an array of predictions for some test data.
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- __Arguments__: Same meaning as fit method above.
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- __predict_classes(X, batch_size=128, verbose=1)__: Return an array of class predictions for some test data.
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- __Arguments__: Same meaning as fit method above. verbose is used as a binary flag (progress bar or nothing).
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- __train__(X, y, accuracy=False)__: Single gradient update on one batch. if accuracy==False, return tuple (loss_on_batch, accuracy_on_batch). Else, return loss_on_batch.
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- __test__(X, y, accuracy=False)__: Single performance evaluation on one batch. if accuracy==False, return tuple (loss_on_batch, accuracy_on_batch). Else, return loss_on_batch.
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__Examples__:
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```python
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from keras.models import Sequential
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from keras.layers.core import Dense, Dropout, Activation
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from keras.optimizers import SGD
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model = Sequential()
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model.add(Dense(64, 2, init='uniform'))
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model.add(Activation('softmax'))
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model.compile(loss='mse', optimizer='sgd')
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# demonstration of verbose modes 1 and 2
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model.fit(X_train, y_train, nb_epoch=3, batch_size=16, validation_split=0.1, verbose=1)
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# outputs
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'''
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Train on 37800 samples, validate on 4200 samples
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Epoch 0
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37800/37800 [==============================] - 7s - loss: 0.0385 - acc.: 0.7258
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Epoch 1
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37800/37800 [==============================] - 8s - loss: 0.0140 - acc.: 0.9265
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Epoch 2
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10960/37800 [=======>......................] - ETA: 4s - loss: 0.0109 - acc.: 0.9420
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'''
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model.fit(X_train, y_train, nb_epoch=3, batch_size=16, verbose=2)
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# outputs
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'''
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Train on 37800 samples, validate on 4200 samples
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Epoch 0
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loss: 0.0190 - acc.: 0.8750
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Epoch 1
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loss: 0.0146 - acc.: 0.8750
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Epoch 2
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loss: 0.0049 - acc.: 1.0000
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'''
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# demonstration of show_accuracy
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model.fit(X_train, y_train, nb_epoch=3, batch_size=16, validation_split=0.1, show_accuracy=True, verbose=1)
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# outputs
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'''
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Train on 37800 samples, validate on 4200 samples
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Epoch 0
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37800/37800 [==============================] - 7s - loss: 0.0385 - acc.: 0.7258 - val. loss: 0.0160 - val. acc.: 0.9136
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Epoch 1
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37800/37800 [==============================] - 8s - loss: 0.0140 - acc.: 0.9265 - val. loss: 0.0109 - val. acc.: 0.9383
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Epoch 2
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10960/37800 [=======>......................] - ETA: 4s - loss: 0.0109 - acc.: 0.9420
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'''
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```
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